SupplyChainTalk: How AI is modernising logistics management 

Views on news 

Amazon has unveiled an upgraded ‌AI-powered mobile robot for its warehouses that can respond to conversational prompts, as part of a €10 billion ($11.6 billion) investment in its European fulfilment network. The new version of its Proteus model due in Europe in the first half of 2027, can operate across warehouse floors and marks a shift in how employees interact with robots, who – being told what needs to be done – figures out the priority, the route and ​the timing. Amazon is also leveraging seasonality and real-time traffic patterns to improve its same day delivery. This move will considerably decrease the time from order to delivery and enhance last mile performance.  

 

Getting the foundation right for implementing AI 

AI today should be seen as a planning excellence tool. While up until recently spreadsheets were the main tools, now, AI enables real-time COGS analysis to get visibility of moving goods and optimise costs. But AI should also be used to clear and improve available data – a prerequisite for efficient AI deployments and the shift from reactive to predictive and adaptive demand planning. There are several ways in which AI can help grow the business without investment in new assets – through driver retention intent to cost savings to increased density. To benchmark your organisation’s transportation ecosystems against peers, you can have general conversations with early adopters and businesses from other sectors who are not competing with you or turn to trade associations. Also, go to your vendors’ user conferences.  

 

With AI incorporated into their planning, businesses can see a 10 per cent stock-out reduction. AI can also improve stock management for short shelf life items. Visibility is key in last mile delivery too and should be achieved at several different levels including vehicles, drivers, as well as traffic, customer order, warehouse and safety data. With unified data systems, reporting will become more straightforward too. The strongest use cases for AI deployment are the ones that combine automation with human judgement. Thankfully, logistics partners are now becoming more willing to share data with suppliers as offering visibility is slowly becoming a competitive advantage.

 

That said, building close relationships with key vendors is also an important factor in achieving visibility. Start your AI deployment with understanding the kinds of business problems you’re having and identifying the repetitive, highly manual tasks in that workflow which can be automated. Set up a data governance framework and make sure you have cross-functional ownership including your frontline teams.  

 

The panel’s advice 

• Planning is a key stage of implementing AI.    
• Make sure your estimated service times are as accurate as possible, leveraging AI. Don’t create unrealistic routes.  
• Instead of thinking about deploying AI, build the foundation that paves the way for it. Ask your vendors about their AI roadmap and how that will impact your business.